我正在寻找 Java 中的 KDTree 实现。
我做了一个谷歌搜索，结果似乎很随意。实际上有很多结果，但它们大多只是一次性的实现，我宁愿找到更多“生产价值”的东西。诸如 apache 集合或 .NET 的优秀 C5 集合库之类的东西。我可以看到公共错误跟踪器并检查最后一次 SVN 提交发生的时间。此外，在理想情况下，我会为空间数据结构找到一个精心设计的 API，而 KDTree 只是该库中的一个类。
对于这个项目，我只会在 2 维或 3 维中工作，而且我主要只是对一个好的最近邻实现感兴趣。
【问题讨论】：
标签： java data-structures kdtree
我已经成功地发现了 Levy 教授的实现 here。我意识到您正在寻找更多经过生产认证的实施，因此这可能不合适。
但是，请注意任何路人，我已经在我的照片马赛克项目中使用它一段时间了，没有任何问题。没有保证，但总比没有好:)
【讨论】：
这是 KD-Tree 的完整实现，我使用了一些库来存储点和矩形。这些库是免费提供的。可以使用这些类创建自己的类来存储点和矩形。请分享您的反馈。
import java.util.ArrayList;
import java.util.List;
import edu.princeton.cs.algs4.In;
import edu.princeton.cs.algs4.Point2D;
import edu.princeton.cs.algs4.RectHV;
import edu.princeton.cs.algs4.StdDraw;
public class KdTree {
private static class Node {
public Point2D point; // the point
public RectHV rect; // the axis-aligned rectangle corresponding to this
public Node lb; // the left/bottom subtree
public Node rt; // the right/top subtree
public int size;
public double x = 0;
public double y = 0;
public Node(Point2D p, RectHV rect, Node lb, Node rt) {
super();
this.point = p;
this.rect = rect;
this.lb = lb;
this.rt = rt;
x = p.x();
y = p.y();
}
}
private Node root = null;;
public KdTree() {
}
public boolean isEmpty() {
return root == null;
}
public int size() {
return rechnenSize(root);
}
private int rechnenSize(Node node) {
if (node == null) {
return 0;
} else {
return node.size;
}
}
public void insert(Point2D p) {
if (p == null) {
throw new NullPointerException();
}
if (isEmpty()) {
root = insertInternal(p, root, 0);
root.rect = new RectHV(0, 0, 1, 1);
} else {
root = insertInternal(p, root, 1);
}
}
// at odd level we will compare x coordinate, and at even level we will
// compare y coordinate
private Node insertInternal(Point2D pointToInsert, Node node, int level) {
if (node == null) {
Node newNode = new Node(pointToInsert, null, null, null);
newNode.size = 1;
return newNode;
}
if (level % 2 == 0) {//Horizontal partition line
if (pointToInsert.y() < node.y) {//Traverse in bottom area of partition
node.lb = insertInternal(pointToInsert, node.lb, level + 1);
if(node.lb.rect == null){
node.lb.rect = new RectHV(node.rect.xmin(), node.rect.ymin(),
node.rect.xmax(), node.y);
}
} else {//Traverse in top area of partition
if (!node.point.equals(pointToInsert)) {
node.rt = insertInternal(pointToInsert, node.rt, level + 1);
if(node.rt.rect == null){
node.rt.rect = new RectHV(node.rect.xmin(), node.y,
node.rect.xmax(), node.rect.ymax());
}
}
}
} else if (level % 2 != 0) {//Vertical partition line
if (pointToInsert.x() < node.x) {//Traverse in left area of partition
node.lb = insertInternal(pointToInsert, node.lb, level + 1);
if(node.lb.rect == null){
node.lb.rect = new RectHV(node.rect.xmin(), node.rect.ymin(),
node.x, node.rect.ymax());
}
} else {//Traverse in right area of partition
if (!node.point.equals(pointToInsert)) {
node.rt = insertInternal(pointToInsert, node.rt, level + 1);
if(node.rt.rect == null){
node.rt.rect = new RectHV(node.x, node.rect.ymin(),
node.rect.xmax(), node.rect.ymax());
}
}
}
}
node.size = 1 + rechnenSize(node.lb) + rechnenSize(node.rt);
return node;
}
public boolean contains(Point2D p) {
return containsInternal(p, root, 1);
}
private boolean containsInternal(Point2D pointToSearch, Node node, int level) {
if (node == null) {
return false;
}
if (level % 2 == 0) {//Horizontal partition line
if (pointToSearch.y() < node.y) {
return containsInternal(pointToSearch, node.lb, level + 1);
} else {
if (node.point.equals(pointToSearch)) {
return true;
}
return containsInternal(pointToSearch, node.rt, level + 1);
}
} else {//Vertical partition line
if (pointToSearch.x() < node.x) {
return containsInternal(pointToSearch, node.lb, level + 1);
} else {
if (node.point.equals(pointToSearch)) {
return true;
}
return containsInternal(pointToSearch, node.rt, level + 1);
}
}
}
public void draw() {
StdDraw.clear();
drawInternal(root, 1);
}
private void drawInternal(Node node, int level) {
if (node == null) {
return;
}
StdDraw.setPenColor(StdDraw.BLACK);
StdDraw.setPenRadius(0.02);
node.point.draw();
double sx = node.rect.xmin();
double ex = node.rect.xmax();
double sy = node.rect.ymin();
double ey = node.rect.ymax();
StdDraw.setPenRadius(0.01);
if (level % 2 == 0) {
StdDraw.setPenColor(StdDraw.BLUE);
sy = ey = node.y;
} else {
StdDraw.setPenColor(StdDraw.RED);
sx = ex = node.x;
}
StdDraw.line(sx, sy, ex, ey);
drawInternal(node.lb, level + 1);
drawInternal(node.rt, level + 1);
}
/**
* Find the points which lies in the rectangle as parameter
* @param rect
* @return
*/
public Iterable<Point2D> range(RectHV rect) {
List<Point2D> resultList = new ArrayList<Point2D>();
rangeInternal(root, rect, resultList);
return resultList;
}
private void rangeInternal(Node node, RectHV rect, List<Point2D> resultList) {
if (node == null) {
return;
}
if (node.rect.intersects(rect)) {
if (rect.contains(node.point)) {
resultList.add(node.point);
}
rangeInternal(node.lb, rect, resultList);
rangeInternal(node.rt, rect, resultList);
}
}
public Point2D nearest(Point2D p) {
if(root == null){
return null;
}
Champion champion = new Champion(root.point,Double.MAX_VALUE);
return nearestInternal(p, root, champion, 1).champion;
}
private Champion nearestInternal(Point2D targetPoint, Node node,
Champion champion, int level) {
if (node == null) {
return champion;
}
double dist = targetPoint.distanceSquaredTo(node.point);
int newLevel = level + 1;
if (dist < champion.championDist) {
champion.champion = node.point;
champion.championDist = dist;
}
boolean goLeftOrBottom = false;
//We will decide which part to be visited first, based upon in which part point lies.
//If point is towards left or bottom part, we traverse in that area first, and later on decide
//if we need to search in other part too.
if(level % 2 == 0){
if(targetPoint.y() < node.y){
goLeftOrBottom = true;
}
} else {
if(targetPoint.x() < node.x){
goLeftOrBottom = true;
}
}
if(goLeftOrBottom){
nearestInternal(targetPoint, node.lb, champion, newLevel);
Point2D orientationPoint = createOrientationPoint(node.x,node.y,targetPoint,level);
double orientationDist = orientationPoint.distanceSquaredTo(targetPoint);
//We will search on the other part only, if the point is very near to partitioned line
//and champion point found so far is far away from the partitioned line.
if(orientationDist < champion.championDist){
nearestInternal(targetPoint, node.rt, champion, newLevel);
}
} else {
nearestInternal(targetPoint, node.rt, champion, newLevel);
Point2D orientationPoint = createOrientationPoint(node.x,node.y,targetPoint,level);
//We will search on the other part only, if the point is very near to partitioned line
//and champion point found so far is far away from the partitioned line.
double orientationDist = orientationPoint.distanceSquaredTo(targetPoint);
if(orientationDist < champion.championDist){
nearestInternal(targetPoint, node.lb, champion, newLevel);
}
}
return champion;
}
/**
* Returns the point from a partitioned line, which can be directly used to calculate
* distance between partitioned line and the target point for which neighbours are to be searched.
* @param linePointX
* @param linePointY
* @param targetPoint
* @param level
* @return
*/
private Point2D createOrientationPoint(double linePointX, double linePointY, Point2D targetPoint, int level){
if(level % 2 == 0){
return new Point2D(targetPoint.x(),linePointY);
} else {
return new Point2D(linePointX,targetPoint.y());
}
}
private static class Champion{
public Point2D champion;
public double championDist;
public Champion(Point2D c, double d){
champion = c;
championDist = d;
}
}
public static void main(String[] args) {
String filename = "/home/raman/Downloads/kdtree/circle100.txt";
In in = new In(filename);
KdTree kdTree = new KdTree();
while (!in.isEmpty()) {
double x = in.readDouble();
double y = in.readDouble();
Point2D p = new Point2D(x, y);
kdTree.insert(p);
}
// kdTree.print();
System.out.println(kdTree.size());
kdTree.draw();
System.out.println(kdTree.nearest(new Point2D(0.4, 0.5)));
System.out.println(new Point2D(0.7, 0.4).distanceSquaredTo(new Point2D(0.9,0.5)));
System.out.println(new Point2D(0.7, 0.4).distanceSquaredTo(new Point2D(0.9,0.4)));
}
}
【讨论】：
package kdtree;
class KDNode{
KDNode left;
KDNode right;
int []data;
public KDNode(){
left=null;
right=null;
}
public KDNode(int []x){
left=null;
right=null;
data = new int[2];
for (int k = 0; k < 2; k++)
data[k]=x[k];
}
}
class KDTreeImpl{
KDNode root;
int cd=0;
int DIM=2;
public KDTreeImpl() {
root=null;
}
public boolean isEmpty(){
return root == null;
}
public void insert(int []x){
root = insert(x,root,cd);
}
private KDNode insert(int []x,KDNode t,int cd){
if (t == null)
t = new KDNode(x);
else if (x[cd] < t.data[cd])
t.left = insert(x, t.left, (cd+1)%DIM);
else
t.right = insert(x, t.right, (cd+1)%DIM);
return t;
}
public boolean search(int []data){
return search(data,root,0);
}
private boolean search(int []x,KDNode t,int cd){
boolean found=false;
if(t==null){
return false;
}
else {
if(x[cd]==t.data[cd]){
if(x[0]==t.data[0] && x[1]==t.data[1])
return true;
}else if(x[cd]<t.data[cd]){
found = search(x,t.left,(cd+1)%DIM);
}else if(x[cd]>t.data[cd]){
found = search(x,t.right,(cd+1)%DIM);
}
return found;
}
}
public void inorder(){
inorder(root);
}
private void inorder(KDNode r){
if (r != null){
inorder(r.left);
System.out.print("("+r.data[0]+","+r.data[1] +") ");
inorder(r.right);
}
}
public void preorder() {
preorder(root);
}
private void preorder(KDNode r){
if (r != null){
System.out.print("("+r.data[0]+","+r.data[1] +") ");
preorder(r.left);
preorder(r.right);
}
}
/* Function for postorder traversal */
public void postorder() {
postorder(root);
}
private void postorder(KDNode r) {
if (r != null){
postorder(r.left);
postorder(r.right);
System.out.print("("+r.data[0]+","+r.data[1] +") ");
}
}
}
public class KDTree {
/**
* @param args the command line arguments
*/
public static void main(String[] args) {
// TODO code application logic here
KDTreeImpl kdt = new KDTreeImpl();
int x[] = new int[2];
x[0] = 30;
x[1] = 40;
kdt.insert(x);
x[0] = 5;
x[1] = 25;
kdt.insert(x);
x[0] = 10;
x[1] = 12;
kdt.insert(x);
x[0] = 70;
x[1] = 70;
kdt.insert(x);
x[0] = 50;
x[1] = 30;
kdt.insert(x);
System.out.println("Input Elements");
System.out.println("(30,40) (5,25) (10,12) (70,70) (50,30)\n\n");
System.out.println("Printing KD Tree in Inorder");
kdt.inorder();
System.out.println("\nPrinting KD Tree in PreOder");
kdt.preorder();
System.out.println("\nPrinting KD Tree in PostOrder");
kdt.postorder();
System.out.println("\nsearching...............");
x[0]=40;x[1]=40;
System.out.println(kdt.search(x));
}
}
【讨论】：
你是对的，没有多少网站有 Java 的 kd 实现！无论如何，kd 树基本上是一个二叉搜索树，通常每次都会为该维度计算中值。这是简单的 KDNode，就最近邻方法或完整实现而言，请查看这个github 项目。这是我能为你找到的最好的。希望对您有所帮助。
private class KDNode {
KDNode left;
KDNode right;
E val;
int depth;
private KDNode(E e, int depth){
this.left = null;
this.right = null;
this.val = e;
this.depth = depth;
}
【讨论】：
【讨论】：
我创建了一个 KD-Tree 实现作为离线反向地理编码库的一部分
【讨论】：
为未来的寻求者。 Java-ml 库有一个运行良好的 kd-tree 实现。 http://java-ml.sourceforge.net/
【讨论】：
在Algorithms in a Nutshell 一书中，Java 中有一个 kd 树实现以及一些变体。所有代码都在oreilly.com 上，本书本身也将引导您完成算法，以便您自己构建一个。
【讨论】：
也许 Stony-Brook 算法存储库中的 Nearest Neighbor Search 和 KD-trees 可以提供帮助。
【讨论】：